Methods and systems for identification of protein classes

ABSTRACT

Methods and computer systems for identifying protein classes from nucleic acid or amino acid sequence data are disclosed.

FIELD OF THE INVENTION

This invention relates to computer-based methods and systems for identification of protein classes, including secreted and non-secreted proteins, in protein or nucleic acid sequence data.

BACKGROUND OF THE INVENTION

Current large-scale sequencing techniques are elucidating numerous novel genes in various eukaryotic and prokaryotic genomes. It is generally recognized that these efforts will be beneficial to the advancement of medicine and biology. To achieve this benefit, identified novel genes must be classified and biological function determined.

Secreted and non-secreted proteins are important classes. Targeting protein drugs into body fluids or onto receptors located on the surface of the cellular membrane is much easier than transporting proteinaceous compounds into the cytosol or the organelles. The relative ease of targeting protein drugs explains why most approved protein drugs are secreted proteins, a trend that is likely to remain so in the foreseeable future. Known secreted proteins include all peptide hormones and their receptors, breast and colon cancer gene products, leptin and its receptor, serum albumin, superoxide dismutase, spliceosome proteins, 7-transmembrane proteins, immunoglobulins, deoxyribonuclease I and several families of serine proteinases.

Secreted proteins are synthesized by a host organism in the cytosol, and in eukaryotes, are exported cotranslationally into the endoplasmatic reticulum and subsequently secreted into the extracellular space or inserted into the membrane. Proteins are transported either across a translocation pore or in direct interaction with lipids.

The vehicles for protein transportation are the mostly amino-terminal signal peptides, extending to 12 to 50 residues in eukaryotes. Signal peptides are the most abundant functional domain in proteins. They consist of three short segments: a usually basic cytosolic part that generally starts with a methionine; a hydrophobic segment; and a cleavage site for the signal peptidase enzyme. In contrast to this sophisticated and effective mechanism, only sporadic natural pathways, such as pinocytosis and membrane punching by performs, exist for the transport in the opposite direction, i.e., from the extracellular space into the cytosol.

Traditional experimental methods for the identification of secreted proteins are labor and cost-intensive. Exemplary methods include peptide sequencing, immunoelectron microscopy and membrane ultracentrifugation.

A more recently developed experimental method for identifying secreted proteins is the DiscoverEase® protocol of Genetics Institute (U.S. Pat. No. 5,536,637). This protocol utilizes a yeast mutant strain defective in the invertase signal peptide and has been used to identify thousands of secreted proteins.

While experimental techniques are indispensable in finding novel types of secretory signals, screening hundreds of thousands of Expressed Sequence Tag (EST) assemblies generated by large-scale sequencing efforts to identify secreted proteins is feasible only by extremely fast computational tools that can predict secreted proteins for large amounts of sequences at a reasonable cost. However, signal peptides are a classic example of how an identical function is preserved across extremely variable sequences (1). In the hydrophobic segment, for instance, the order of leucine, valine, isoleucine and other hydrophobic residues seems to be irrelevant to function (2). As a result of this variation, signal peptides cannot be identified by the usual sequence analysis methods like BLAST (3), FASTA (4,5) or dynamic programming (6), all of which are based on the conservation of sequences.

Gunnar von Heijne (7) has created a profile-like method for the recognition of the cleavage site, the least variable segment of signal peptides, that works with an accuracy of about 70% on protein data. His method has been coded into computer programs by several authors, including Pascarella and Bossa (8), Folz and Gordon (9) and Popowicz and Dash (10). Attempts to improve the accuracy of this method by increasing the minimal score eliminates several false positives only at the expense of false rejection of bona fide signal peptides.

Neural networks are more accurate tools for the prediction of signal peptides. Ladunga et al. (11) applied the so-called tiling algorithm (12) for the amino-terminal 20 residues of signal peptides in combination with von Heijne's method. Nielsen et al. (13,14) used a standard feed-forward algorithm trained by the back-propagation algorithm (15). In the production system created by Nakai, Kanehisa and coworkers (16-18), decision rules are applied in the form of binary decision trees. A fundamentally different approach (19) predicts signal peptides by modeling the angle of insertion into the membrane and the orientation of the signal peptide in the yeast invertase system. For a recent review on computational methods to predict secreted proteins, see (20).

Despite the current efforts, a need exists to increase the precision of computational methods for identifying secreted proteins. Since large-scale sequencing efforts produce nucleotide rather than amino acid sequence data, the accuracy of predicting secreted proteins will depend upon the sequencing accuracy, the reading frame prediction accuracy and the accuracy of the prediction of the signal peptide/membrane anchor on the protein level. Taking into account the rate of sequencing errors in ESTs (including artificial frameshifts), and the uncertainty in finding the correct reading frame and translation initiation site, the identification of secreted proteins from EST data will require significantly more accurate computational predictions than the ones described above.

SUMMARY OF THE INVENTION

Accordingly, one aspect of the invention is a method for identifying protein classes from protein or nucleic acid sequence data comprising analyzing combinations of amino acid properties by mathematical programming.

Another aspect of the invention is a method for identifying protein classes from protein or nucleic acid sequence data comprising:

a) determining the average amino acid property values of non-overlapping oligopeptide windows in query sequence data and a protein reference set;

b) weighting the reference set values; and

c) distinguishing between protein classes.

Another aspect of the invention is a method for identifying secreted and non-secreted proteins from protein or nucleic acid sequence data comprising:

a) determining the average amino acid property values of non-overlapping oligopeptide windows in reference sets of experimentally known secreted and non-secreted proteins;

b) determining weights of the determined property values;

c) identifying a putative amino terminus in query sequence data;

d) determining the average amino acid property values of non-overlapping oligopeptide windows in the putative amino terminus of the query sequence data; and

e) distinguishing between secreted and non-secreted proteins.

Yet another aspect of the invention is computer systems for performing the methods of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a demonstrative example of the linear separation of secreted and cytosolic proteins in weight optimization using two amino acid properties.

DETAILED DESCRIPTION OF THE INVENTION

In the present invention, it has been observed that a properly weighted subset of a finite number of physicochemical, physical, chemical, biological, structural, steric, etc., properties of the amino acids can provide accurate distinction between different classes of proteins, such as secreted and non-secreted. Distinction between other protein classes such as chemokines, 7-transmembrane receptors, neuropeptides and the location of the cleavage site for the signal peptidase enzyme could also be achieved by the present invention.

Robust evolutionary conservation of amino acid properties that determine the fold of proteins (hydrophobicity measures, steric properties, etc.) has been observed at positions of multiple alignments of protein sequences in nine large multiple alignment databases (21). It has been shown that the best conserved properties are essential for the folding of the protein, i.e. the conservation of these properties conserves the fold of the protein. Conservation of these properties can be of diagnostic value even when the sequences themselves are not conserved. A classic example for that is signal peptides and membrane anchors: in spite of the tremendous variation in sequence, certain properties, such as those related to hydrophobicity, are well-conserved at certain positions of multiple alignments of these peptides. While the applicant does not intend to be bound by a particular theory, it is highly unlikely that these properties were conserved by chance alone in large samples. A more plausible explanation is the elimination of dysfunctional (improperly folded) mutants by natural selection. This implies that conservation of a property may indicate its functional and structural importance, i.e., more conserved properties are better indicators of a protein class, such as whether an unknown sequence contains a signal peptide or a membrane anchor or the amino-terminal segment of a cytosolic protein.

As used herein, the term "protein classes" refers to protein classifications such as secreted and non-secreted, chemokine, 7-transmembrane receptor, to protein domains such as signal peptidase cleavage site, and others.

As used herein, the term "secreted proteins" refers to proteins secreted into the extracellular space or into a cellular organelle (nucleus, mitochondrion, lysosome, chloroplast, etc.) or into a membrane, i.e., membrane-bound proteins. The term "non-secreted proteins" includes any proteins that are not translocated across biological membranes, such as cytosolic proteins.

As used herein, the term "nucleic acid sequence data" refers to a series of symbolic codes for deoxynucleotide or ribonucleotide bases. The term "protein sequence data" refers to a series of symbolic codes for amino acid residues. Both types of sequence data can be derived by automated or manual methods well known to those skilled in the art and can be stored in a database.

As used herein, the term "amino acid properties" refers to any of the different physical, chemical, physicochemical, biochemical, structural, steric and other properties (indices) of amino acids. The term "amino acid property values" refers to sets of 20 numerical values representing the different properties of amino acids. The amino acid properties include, but are not limited to, the 402 properties (indices) including propensities to form alpha-helices, beta-structures, composition, hydrophobicity, physicochemical and other properties that are listed together with their non-normalized values in the Amino Acid Index database (AAINDEX) available by Internet file transfer protocol from ftp.genome.ad.jp/db/genomenet/aaindex/. See also (22,23). AAINDEX entries are listed below after the Example section. In the method of the invention, comparison of amino acid properties with different units of measurement is facilitated by normalizing the values of each property to a range. Preferably, the range is -100 to +100.

The present invention provides for novel prediction methods of protein classes such as secreted proteins in nucleic acid or protein sequence data. The methods of the invention are computer-based. Despite an extreme sequence variation in the subdomains of signal peptides, there are characteristic patterns of amino acid properties for these subdomains. In the method of the present invention, it has been determined that analysis of combinations of amino acid property values by mathematical, i.e., linear, integer, mixed integer or quadratic, programming, allows selective and sensitive distinction between protein classes such as secreted and non-secreted proteins. The method provides a higher level of generalization as compared to direct sequence information.

Potential novel protein sequences can be identified by the method of the invention. The sequences identified by the method can be stored in a database and characterized for biological function. The nucleic acids and proteins represented by the sequence data are useful as potential drugs, targets for potential drugs, drug discovery reagents and/or biomedical research tools.

The method of the invention processes either protein or nucleic acid sequence data, the latter being translated in six reading frames. All segments having a putative amino terminus can be analyzed by the method of the invention. Putative amino termini are identified by location of a start site, i.e, a translation initiation codon or an initiating amino acid residue such as methionine in query sequence data. In the case of nucleic acid sequence data, segments having a translation initiation codon are analyzed provided that: (i) they do not contain STOP codons or ambiguous amino acid codons within a minimum of 30 codons; and (ii) their amino acid composition is within the compositional range of known signal peptides in the reference set or its subsets. Exemplary translation initiation codons are NTG, ATC or ATT. Optionally, Kozak's (24) rules can be enforced for identification of the translation initiation sites.

Inputs to the method of the invention are the query sequences (protein or nucleic acid), the normalized amino acid property values of the 20 amino acids, the weights of the selected properties, and the compositional ranges of the windows.

Output for both nucleic acid and protein sequence queries includes the position of the initiation codon, the distance ratio for the closest non-signal reference protein to the closest signal reference protein, the distance ratio for the second closest non-signal reference to the second closest signal reference and the amino acid sequence of the identified signal peptide. For nucleic acid data, the frame of translation is also included as output.

One embodiment of the present invention provides a method for identifying protein classes from protein or nucleic acid sequence data comprising analyzing combinations of amino acid properties by mathematical programming.

In another embodiment of the present invention, the average amino acid property values of non-overlapping oligopeptide windows in query sequence data and a protein reference set are determined, the reference set values are weighted and the values used to distinguish between protein classes.

In each of the above embodiments, exemplary protein classes are secreted and non-secreted, chemokine, 7-transmembrane receptor and signal peptidase cleavage site. Preferably, the protein classes are secreted and non-secreted. Also preferred is the method wherein the number of amino acid properties analyzed is about 17 to about 25.

In another embodiment of the present invention, secreted proteins are identified from protein or nucleic acid sequence data in a method comprising:

a) determining the average amino acid property values of non-overlapping oligopeptide windows in reference sets of experimentally known secreted and non-secreted proteins;

b) determining weights of the determined property values;

c) identifying a putative amino terminus in query sequence data;

d) determining the average amino acid property values of non-overlapping oligopeptide windows in the putative amino terminus of the query sequence data; and

e) distinguishing between secreted and non-secreted proteins.

In the method of the invention, the amino acid properties of amino terminal segments of the reference sets of experimentally known secreted and non-secreted proteins and of the putative amino termini of query sequence data are determined. Preferably, the amino terminal segment length is 30 residues, as the typical length of an eukaryotic signal peptide is about 24 residues, and some additional amino-terminal residues of the passenger protein are also crucial in determining whether the protein can or cannot be transported (7). Each segment of peptide and translated nucleic acid sequence data is broken down into windows of nonoverlapping oligopeptides. Preferably, the oligopeptides are pentapeptides. Each segment s is broken down into six windows w of nonoverlapping pentapeptides. Averages of each normalized amino acid property value k over the m resides (where m=5 for a pentapeptide) involved in oligopeptide window w are calculated by: ##EQU1## where c(s,w,k) is the average value of property value k in window w of segment s; and p(i,k) is the value of property value k for window position i. For example, if a segment's third window contains amino acid residues LIAVM, the hydropathy of this window will be the average hydropathy of leucine, isoleucine, alanine, valine and methionine.

Distinguishing secreted proteins from non-secreted proteins using n properties requires separation of that property space into two respective parts. As secreted and cytosolic proteins are linearly inseparable in the property space used here, a distance method is used to distinguish these protein classes.

Distances d(q,s) between the query segment q and each signal peptide s (and cytosolic segment y) are calculated: ##EQU2## where u(w,k) is the weight for property k in window w of segment s. To increase the speed of calculation, absolute value distances are preferred to Euclidean distances. These distances are compatible: if for the absolute value distances among any three points A, B and C, d(A,B)<d(A,C), then the same relation holds for the Euclidean distances as well.

Distances to the closest secreted protein and cytosolic protein, respectively, are selected and their ratio is computed. The query sequence is predicted either as a signal peptide or a membrane anchor, i.e., a secreted protein, provided that (i) the distance to the closest secreted protein does not exceed the largest distance between any pair of the known secreted proteins in the reference set; and (ii) the ratio of the distances to the closest nonsecreted versus the closest secreted protein exceeds a specified threshold. Preferably, the specified threshold is ≧1.0. Most preferably, the specified threshold is ≧1.3, as no cytosolic protein in the reference set was observed with a distance ratio of 1.29 or above during the cross-validation experiments (see Example 1, infra).

The value c(s,w,k) representing the average amino acid property value derived from the reference set is weighted to reflect its diagnostic value in distinguishing signal peptides or membrane anchors from cytosolic proteins. Proper weighting provides for maximization of the number of correctly predicted sequences (both secreted and cytosolic proteins). This is equivalent to the minimization of misclassifications. Property weights u(w, k) are calculated by (i) according evolutionary conservation of the property from which the importance in determining protein folds can be inferred; (ii) neural networks or genetic algorithm techniques; or (iii) by large-scale optimization using mathematical programming, which includes integer, mixed-integer, linear or quadratic programming. Preferably, mathematical programming is used.

In one embodiment of the invention, variation, or its inverse, conservation, is measured. Preferably, variation is measured. For each window w, property k and each signal peptide s in the reference set, p(s,w,k) is calculated (the arithmetic mean of the propensity values of the 5 residues in window w and sequence s). The variation of property k at window w is then defined as the R(secr,w,k) range of p(s,w,k) values over all signal peptides. The variations R(cyt,w,k) for windows and properties of non-secreted (cytosolic) proteins are calculated in an analogous manner.

The ratio ##EQU3## where μ(secr, w, k) and μ(cyt,w, k) are the average values of property k in window w and expresses the diagnostic value of property k in window w in distinguishing signal peptides or membrane anchors from cytosolic proteins. The u(w,k) weight of a property k at window w is given by: ##EQU4## The weights for all properties and windows are normalized to yield a sum of 10,000. As an example, Table 1 shows the 20 properties with the highest weight as calculated by evolutionary conservation (Eqs. 3-4) for pentapeptide No. 4 of the reference set (See Example 1, infra).

                  TABLE 1                                                          ______________________________________                                         Amino Acid Property Value Weights Calculated on the                            Basis of Evolutionary Conservation                                             Property                   Weight                                              ______________________________________                                         Average relative fractional occurrence in AL(i)                                                           79                                                  Polarity                   66                                                  Parameter of charge transfer capability                                                                   65                                                  Relative population of conformational state C                                                             53                                                  Normalized frequency of turn in all-alpha class                                                           53                                                  Hydration number           53                                                  Boltzmann probability of backbone conformational states                                                   52                                                  Normalized relative frequency of extended structure                                                       45                                                  Normalized frequency of zeta R                                                                            45                                                  Normalized frequency of extended structure                                                                45                                                  Information measure for extended without hydrogene bond                                                   44                                                  Polar requirement          43                                                  Principal component IV     42                                                  Normalized relative frequency of bend S                                                                   42                                                  Weights for alpha-helix at the window position of -6                                                      41                                                  Hydrophobicity             41                                                  Weights for beta-sheet at the window position of -3                                                       40                                                  Structural information content, mean                                                                      39                                                  Retention factor of principal dinucleotides in high salt                                                  39                                                  chromatography                                                                 Short and medium-range non-bonded energy per residue                                                      38                                                  ______________________________________                                    

In another embodiment of the invention, property weights are determined by maximizing how secreted proteins can be discriminated from cytosolic proteins. Property weights are optimized by mathematical programming, simulated neural networks or genetic algorithms. A brute force optimization of N(dist) distances between all possible pairs of sequences for all windows: ##EQU5## where N(w) is the number of windows and N(seq) is the number of sequences in the reference set, is practically impossible. In application to the refrence set below, the number of distances would be on the order of 10⁷. Taking into account that each distance can involve 402 or more dimensions, a problem of that size exceeds any currently available hardware capacity and CPU limitations.

Therefore, weights are instead optimized for a simplified model: the linear separation by an n-1 dimensional cutting plane (25). Determination of weights that provide optimal linear separation between secreted and cytosolic proteins were demonstrated in the cross-validation experiments in Example 1, infra, to provide even better separation for the more sensitive and selective distance method.

A fictitious example of a two-dimensional space with a one-dimensional cutting plane (straight line) is shown in FIG. 1. Secreted proteins above a defined dead zone (marked by dotted lines) surrounding the cutting plane (solid line) and non-secreted proteins below the dead zone are considered as correct classifications.

The set of secreted proteins may be concave, i.e., there may exist one or more lines connecting two secreted proteins that contain(s) non-secreted protein(s). In this case, the two classes cannot be separated by a cutting plane or a simple non-linear surface.

Preferably, weights for different properties in different windows are determined and optimized by mathematical programming (26) which results in maximization of the number of correct classifications in the reference set. Most preferred is the use of mixed-integer programming techniques (27-29). This sensitive and selective, highly CPU-intensive, branch of optimization finds the global maximum or minimum of a goal function through optimization of variables. Bounds and conditions should allow a solution, otherwise the model is not feasible. Once a linear or piecewise linear model can be formulated, linear programming can handle even millions of variables and conditions using powerful algorithms and software developed over the last five decades and known to those skilled in the art. See, e.g., Barnhart (30) who describes a large-scale multi-commodity flow problem. The mathematical programming model optimizes the weights (slopes) for each property as well as the vector product g=wx. The subset of the number of properties (dimensions) for each window that allows correct classification for the highest number of secreted and non-secreted proteins in the reference set is selected. Preferably, the subset is about 17 to about 25 properties.

A perfect cutting plane may not exist for a chosen reference set and properties, i.e., there may be no subsets of properties and their optimized weights that allow linear separation between secreted proteins s and cytosolic proteins y. In such cases, Robust Linear Programming (RLP) (31) can be used to minimize the sum of the distances of misclassified sequences from the cutting plane as follows: ##EQU6## where ideally, α(s) is positive for most secreted proteins s and α(y) is negative for most cytosolic proteins y; N(p) is the number of amino acid properties selected; u(w,k) is the weight for property k in window w; γ is the location of the cutting plane given by the vector product wx, and 1-(-1)=2 is the width of the dead zone where any protein is considered as misclassified. Due to the possible linear inseparability of the convex hulls, some proteins may end up on the wrong side of the cutting plane that separated most of the secreted proteins from most cytosolic proteins in the property space. In RLP, the (weighted) sum of the extents of violations (the negative α(s) and the positive α(y) values) are minimized.

RLP has a basic shortcoming. Errors in the reference sets (annotating some secreted proteins as "non-secreted" or vice versa) can produce large "violations" exceeding the effect of several real violations hence leading to the inappropriate allocation of the cutting plane. Therefore, in the method of the invention, the number of misclassifications, rather than the sum of the extents of violations, have been minimized by applying mixed integer programming or special ordered sets (27-29). We maximize the number of correct classifications: for each secreted protein s, C(s) was assigned 1 if α(s) was positive, otherwise 0; for each cytosolic protein y, C(y) was assigned 1 if α(y) was negative, otherwise 0: ##EQU7## Variables to be optimized are the weights for each window and each property, the special ordered set or binary variables C(s) and C(y) indicating the correctness of the classification for each sequence and γ. The speed of optimization was greatly enhanced by specifying upper and lower bounds, solving priorities and by initializing values for most variables as well as by prescribing branching orders for the optimization of binary variables by declaring priorities in the mixed integer models (27). Weights for each selected property is assigned the absolute value of the weights u(w,k) in (Eqs. 5-6) by maximizing the goal function in Eq. 8 above. As a rule, about 20 properties were assigned non-zero weights. Their absolute values were taken and were subsequently normalized to produce a sum of 100. Examples of optimized weights for pentapeptide No. 4 of the reference set (see Example 1, infra) optimized by using special ordered sets are shown in Table 2. All other properties were assigned a weight of zero.

                  TABLE 2                                                          ______________________________________                                         Optimized Weights of Amino Acid Property Values                                Property                     Weight                                            ______________________________________                                         A parameter defined from the residuals obtained from the                                                    7.47                                              correlation                                                                    A parameter of charge transfer donor capability                                                             1.31                                              Free energy change of epsilon(i) to epsilon(ex)                                                             4.01                                              Free energy change of epsilon(i) to alpha(Rh)                                                               1.21                                              Flexibility parameter for two rigid neighbors                                                               1.53                                              Average relative probability of beta-sheet                                                                  18.95                                             Free energy of solution in water                                                                            9.19                                              Hydrophobicity, Zimmerman    0.10                                              Number of bonds in the longest chain                                                                        2.91                                              Number of atoms in the side chain labelled 1 + 1                                                            7.26                                              Net charge                   2.26                                              Dependence of partition coefficient on ionic strength                                                       4.53                                              Weights for alpha-helix at the window position of 6, Qian                                                   4.56                                              Sejnowski                                                                      Weights for alpha-helix at the window position of -1, Qian                                                  1.77                                              Sejnowski                                                                      Weights for beta-sheet at the window position of 3, Qian                                                    5.53                                              Sejnowski                                                                      Weights for beta-sheet at the window position of -3, Qian                                                   8.26                                              Sejnowski                                                                      Spin-spin coupling constants 3JHalpha-NH                                                                    8.26                                              Chemical shift of the alpha-carbon 1H in random coils                                                       10.27                                             Chemical shift of the alpha-carbon 1H in all secondary structures                                           6.16                                              Chemical shift of the amino 1H in alpha-helices                                                             0.35                                              ______________________________________                                    

Upon completion of the method of the invention, the identified sequences are stored in a database. The sequences may be further characterized on the basis of their sequence, structure, biological function or other related characteristics, which information can be linked to the sequences in the database.

One method of further characterizing protein or nucleic acid sequences is by homology to other known genes. Shared homology of a sequence with a known gene may indicate a similar biological role of function. In addition to this method, which can be automated, proteins identified by the method of the invention may also be characterized on the basis of expert commentary from relevant human specialists or by the results of biological experiments.

Another aspect of the invention is a computer system for identifying protein classes, such as secreted or non-secreted proteins, from nucleic acid or protein sequence data. A representative computer system includes a hardware environment on which the methods of the invention may be implemented. The hardware environment includes a central processing unit, a memory device, a display and a user interface device. Exemplary hardware environments are a Sun Microsystems Ultrasparc or Digital Equipment Corporation Alpha workstation, having a display and keyboard and/or mouse input devices.

In one embodiment, the computer system comprises means for analyzing combinations of amino acid properties by mathematical programming.

In another embodiment, the computer system comprises:

a) means for determining the average amino acid property values of non-overlapping oligopeptide windows in query sequence data and a protein reference set;

b) means for weighting the reference set values; and

c) means for distinguishing between protein classes.

In another embodiment, the computer system comprises:

a) means for determining the average amino acid property values of non-overlapping oligopeptide windows in reference sets of experimentally known secreted and non-secreted proteins;

b) means for determining weights of the determined property values;

c) means for identifying a putative amino terminus in query sequence data;

d) means for determining the average amino acid property values of non-overlapping oligopeptide windows in the putative amino terminus of the query sequence data; and

e) means for distinguishing between secreted and non-secreted proteins.

In another embodiment, the computer system comprises:

a) means for determining the average amino acid property values of non-overlapping oligopeptide windows in reference sets of experimentally known secreted and non-secreted proteins comprising:

a1) means for determining amino acid property values in known signal peptides of secreted proteins and amino-termini of non-secreted proteins;

a2) means for normalizing the values of each property to a range; and

a3) means for selecting a subset of the most discriminative or conserved properties;

b) means for determining weights of the determined property values by mathematical programming;

c) means for identifying a putative amino terminus in query sequence data;

d) means for determining the average amino acid property values of non-overlapping oligopeptide windows in the putative amino terminus of the query sequence data;

e) means for calculating distance values between the average physicochemical property values determined by means a) and d);

f) means for identifying the secreted and non-secreted reference proteins having the closest distance to the query sequence; and

g) means for distinguishing between secreted and nonsecreted proteins comprising:

g1) means for determining a ratio of the distances obtained by means e); and

g2) means for predicting the query sequence as a secreted protein when the distance to the closest signal does not exceed the largest distance between any pair of the known signal peptides in the reference set and the ratio of the distances determined by means g1) is ≧1.0.

Preferably, in the systems of the invention, the oligopeptide windows are pentapeptides and the ratio of the distances is ≧1.3.

Preferably, in the systems of the invention, the protein classes are secreted and non-secreted, chemokine, 7-transmembrane receptor and signal peptidase cleavage site. Most preferably, the protein classes are secreted proteins and non-secreted proteins.

Any of the computer system embodiments can further comprise means for characterizing the potential gene or protein sequences.

In another embodiment, the computer system comprises a central processing unit executing a secreted protein identifying program stored in a memory device accessed by the central processing unit; a display on which the central processing unit displays screens of the secreted protein identifying program in response to user inputs; and a user interface device. As used herein, the term "secreted protein identifying program" refers to a program encoding any of the methods of the invention.

The present invention will now be described with reference to the following specific, non-limiting example.

EXAMPLE 1 Analysis of Protein Reference Set

Well-documented reference sets of secreted (including membrane-bound) and non-secreted (cytosolic) proteins were used to train the amino acid property distance method and test prediction accuracy. These sets were imported from Nielsen et al.'s (13,14) World Wide Web server (ftp://virus.cbs.dtu.dk/pub/signalp) and are herein incorporated by reference as though fully set forth. The proteins in the sets were selected from the SWISS-PROT Database of Protein Sequences (Version 29, (32)). Nielsen et al., supra, have discarded any signal peptide or membrane anchor identified by computer methods and entries with questionable evidence or alternative cleavage sites. Their eukaryotic sets include 2,282 eukaryotic signal peptides, 97 uncleaved membrane anchors and 854 cytosolic proteins, respectively.

In the reference sets of eukaryotic secreted and cytosolic proteins, averaged properties were calculated for 6 nonoverlapping pentapeptide windows (the amino-terminal 30 residues) of each sequence in accordance with Eq. 1. Optimization of weights by mathematical programming was performed on a SUN HP10000 workstation with 8 Gigabyte of memory using the CPLEX Package (33) under the AMPL (A Modeling Language for Mathematical Programming)(34) interface. Each pentapeptide window of each sequence was represented as a point in the high dimensional abstract space of the properties with non-zero weights.

The method was tested by random cross-validation (35). In each of 10 repetitions, three quarters of the sequences in the reference sets were selected by random number generator and declared as a training set, while the remaining one quarter was assigned as a test set. Only sequences in the training set were used for weight optimization for the amino acid property distance method and were used in the distance calculations. Predictions were performed on the test set and the results were compared to the experimental assessment of the cellular location of the sequences. Performance of the method was measured by a correlation coefficient r (36): ##EQU8## where TP and TN stand for true positives and negatives, FP and FN for false positives and negatives, respectively. Percentages of true/false positives and negatives, correlation coefficients for the cross-validation experiments on the subsets of eukaryotic secreted and cytosolic reference sets are shown in Table 3. On average, a correlation coefficient of 0.83 was achieved with over 93 percent correct predictions using a distance ratio of 1. The highest distance ratio for cytosolic proteins was 1.29. Thus, the probability of predicting a cytosolic protein as a secreted protein with distance ratios of 1.3 or higher is 1/3000. It is well known that cross-validation underestimates the accuracy of the method of the invention since all available sequences were not used for the training set. Depending on user requirements, the distance ratio threshold can be increased and will eliminate false positives (cytosolic proteins predicted as secreted) at the expense of increasing the number of false negatives (secreted proteins predicted as cytosolic).

                                      TABLE 3                                      __________________________________________________________________________     Test                                                                               No. of sequences                                                                         Correct                                                                               Correl                                                                             Percent true                                                                          Percent false                                  set No.                                                                            All                                                                               Secr.                                                                              Cyto.                                                                             #  %   coeff.                                                                             pos.                                                                              neg.                                                                               pos                                                                               neg.                                        __________________________________________________________________________     1   777                                                                               569 208                                                                               729                                                                               93.82                                                                              0.84                                                                               95.25                                                                             89.90                                                                              10.10                                                                             4.75                                        2   783                                                                               605 178                                                                               734                                                                               93.74                                                                              0.82                                                                               95.70                                                                             87.08                                                                              12.92                                                                             4.30                                        3   732                                                                               537 195                                                                               692                                                                               94.54                                                                              0.86                                                                               95.90                                                                             90.77                                                                              9.23                                                                              4.10                                        4   748                                                                               555 193                                                                               692                                                                               92.51                                                                              0.81                                                                               94.77                                                                             86.01                                                                              13.99                                                                             5.23                                        5   749                                                                               556 193                                                                               707                                                                               94.39                                                                              0.85                                                                               96.94                                                                             87.05                                                                              12.95                                                                             3.06                                        6   740                                                                               553 187                                                                               697                                                                               94.19                                                                              0.85                                                                               96.02                                                                             88.77                                                                              11.23                                                                             3.98                                        7   789                                                                               581 208                                                                               733                                                                               92.90                                                                              0.82                                                                               93.98                                                                             89.90                                                                              10.10                                                                             6.02                                        8   827                                                                               619 208                                                                               762                                                                               92.14                                                                              0.80                                                                               93.05                                                                             89.42                                                                              10.58                                                                             6.95                                        9   773                                                                               592 181                                                                               710                                                                               91.85                                                                              0.77                                                                               95.10                                                                             81.22                                                                              18.78                                                                             4.90                                        10  781                                                                               579 202                                                                               730                                                                               93.47                                                                              0.83                                                                               95.16                                                                             88.61                                                                              11.39                                                                             4.84                                        Average                                                                            770                                                                               575 195                                                                               -- 93.36                                                                              0.83                                                                               95.19                                                                             87.87                                                                              12.13                                                                             4.81                                        __________________________________________________________________________

All programs but the optimization were written in the C of PERL programming languages on UNIX platforms running on SUN Ultrasparc and DEC Alpha workstations.

REFERENCES

All publications from the scientific literature cited in this specification are herein incorporated by reference as though fully set forth.

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Schittkowski, K. ed. Computational Mathematical Programming, Springer, Berlin (1985).

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It will be apparent to those skilled in the art that various modifications can be made to the present method without departing from the scope or spirit of the invention, and it is intended that the present invention cover modifications and variations of the method provided they come within the scope of the appended claims and their equivalents. 

I claim:
 1. A computer-based method for identifying protein classes from protein or nucleic acid sequence data comprising:a) determining the average amino acid property values of non-overlapping oligopeptide windows in query sequence data and a protein reference set; b) weighting the reference set values; and c) distinguishing between protein classes.
 2. The method of claim 1 wherein the protein classes are secreted and non-secreted, chemokine, 7-transmembrane receptor or signal peptidase cleavage site domain.
 3. The method of claim 2 wherein the protein classes are secreted and non-secreted and the number of amino acid properties analyzed is about 17 to about
 25. 4. A computer-based method for identifying secreted proteins from protein or nucleic acid sequence data comprising:a) determining the average amino acid property values of non-overlapping oligopeptide windows in reference sets of experimentally known secreted and non-secreted proteins; b) determining weights of the determined reference set property values; c) identifying a putative amino terminus in query sequence data; d) determining the average amino acid property values of non-overlapping oligopeptide windows in the putative amino terminus of the query sequence data; and e) distinguishing between secreted and non-secreted proteins.
 5. The method of claim 4 wherein the step of distinguishing between secreted and non-secreted proteins comprises the step of separating secreted and non-secreted proteins with a cutting plane or a nonlinear surface.
 6. The method of claim 4 wherein the step of distinguishing between secreted and non-secreted proteins comprises the steps of:e1) calculating distance values between the average physicochemical property values determined in steps a) and d); e2) identifying the secreted and non-secreted reference proteins having the closest distance to the query sequence; e3) determining a ratio of the distances obtained in e2); and e4) predicting the query sequence as a secreted protein when the distance to the closest secreted protein does not exceed the largest distance between any pair of the known secreted proteins in the reference set and the ratio of the distances determined in e3) is a specified threshold value.
 7. The method of claim 6 wherein the specified threshold value is ≧1.0.
 8. The method of claim 6 wherein the specified threshold value is ≧1.3.
 9. The method of claim 4 wherein the number of amino acid properties analyzed is about 17 to about
 25. 10. The method of claim 4 wherein the weights of the determined property values are determined according to their conservation or variation in secreted and non-secreted proteins.
 11. The method of claim 4 wherein the weights of the determined property values are determined by mathematical programming.
 12. The method of claim 4 wherein the determination of the average amino acid property values for the secreted and non-secreted protein reference set comprises the steps of:a. determining physicochemical properties of amino acids in known signal peptides of secreted proteins and amino-termini of non-secreted proteins; b. normalizing the values of each property to a range; and c. selecting a subset of the most discriminative or conserved properties.
 13. The method of claim 4 wherein the oligopeptide windows are pentapeptides.
 14. A computer-based method for identifying secreted proteins from protein or nucleic acid sequence data comprising:a) determining the average amino acid property values of non-overlapping oligopeptide windows in reference sets of experimentally known secreted and non-secreted proteins by the steps of:a1) determining physicochemical properties of amino acids in known signal peptides of secreted proteins and amino-termini of non-secreted proteins; a2) normalizing the values of each property to a range; and a3) selecting a subset of the most discriminative or conserved properties; b) determining weights of the determined property values by mathematical programming; c) identifying a putative amino terminus in query sequence data; d) determining the average amino acid property values of non-overlapping oligopeptide windows in the putative amino terminus of the query sequence data; e) calculating distance values between the average physicochemical property values determined in steps a) and d); f) identifying the secreted and non-secreted reference proteins having the closest distance to the query sequence; and g) distinguishing between secreted and nonsecreted proteins by the steps of:g1) determining a ratio of the distances obtained in step e); and g2) predicting the query sequence as a secreted protein when the distance to the closest signal does not exceed the largest distance between any pair of the known signal peptides in the reference set and the ratio of the distances determined in step g1) is ≧1.0.
 15. The method of claim 14 wherein the oligopeptide windows are pentapeptides.
 16. The method of claim 14 wherein the ratio of the distances is ≧1.3.
 17. The method of claim 14 wherein the subset of the most discriminative or conserved properties is about 17 to about 25 properties.
 18. A computer system for identifying protein classes from protein or nucleic acid sequence data comprising:a) means for determining the average amino acid property values of non-overlapping oligopeptide windows in query sequence data and a protein reference set; b) means for weighting the reference set values; and c) means for distinguishing between protein classes.
 19. A computer system for identifying secreted proteins from protein or nucleic acid sequence data comprising:a) means for determining the average amino acid property values of non-overlapping oligopeptide windows in reference sets of experimentally known secreted and non-secreted proteins; b) means for determining weights of the determined property values; c) means for identifying a putative amino terminus in query sequence data; d) means for determining the average amino acid property values of non-overlapping oligopeptide windows in the putative amino terminus of the query sequence data; and e) means for distinguishing between secreted and non-secreted proteins.
 20. A computer system for identifying secreted proteins from protein or nucleic acid sequence data comprising:a) means for determining the average amino acid property values of non-overlapping oligopeptide windows in reference sets of experimentally known secreted and non-secreted proteins comprising:a1) means for determining amino acid property values in known signal peptides of secreted proteins and amino-termini of non-secreted proteins; a2) means for normalizing the values of each property to a range; and a3) means for selecting a subset of the most discriminative or conserved properties; b) means for determining weights of the determined property values by mathematical programming; c) means for identifying a putative amino terminus in query sequence data; d) means for determining the average amino acid property values of non-overlapping oligopeptide windows in the putative amino terminus of the query sequence data; e) means for calculating distance values between the average physicochemical property values determined by means a) and d); f) means for identifying the secreted and non-secreted reference proteins having the closest distance to the query sequence; and g) means for distinguishing between secreted and nonsecreted proteins comprising:g1) means for determining a ratio of the distances obtained by means e); and g2) means for predicting the query sequence as a secreted protein when the distance to the closest signal does not exceed the largest distance between any pair of the known signal peptides in the reference set and the ratio of the distances determined by means g1) is ≧1.0.
 21. The computer system of claim 20 wherein the oligopeptide windows are pentapeptides.
 22. The computer system of claim 20 wherein the ratio of the distances is ≧1.3.
 23. A computer system for identifying protein classes from protein or nucleic acid sequence data comprising:(a) a central processing unit; (b) a memory device having an executable secreted protein identifying program stored therein whereby the program determines average amino acid property values of non-overlapping oligopeptide windows in query sequence data and a protein reference set, weights the reference set values, and distinguishes between protein classes; (c) a computer display on which the central processing unit displays screens of the secreted protein identifying program in response to user inputs; and (d) a user interface device.
 24. The computer system of claims 18 or 23 wherein the protein classes are secreted and non-secreted, chemokine, 7-transmembrane receptor or signal peptidase cleavage site domain. 